The purpose of this project was to use Python to perform Exploratory Data Analysis (EDA) on the world's most streamed songs of 2023. The objective was to identify dominating artists and uncover audio patterns (such as 'danceability' and 'energy') that correlate with a global hit.
Tools: Python (Jupyter Notebook / Google Colab)
Libraries: Pandas, Matplotlib, Seaborn
Skills Applied: Data Import, Data Structuring (DataFrames), Data Visualization (Bar plots, Scatter plots), Correlation Analysis.
Which artists produced the most hits in 2023, and is there a distinct correlation between a track's measured "danceability" and "energy" among the top-streamed songs?
The data was sourced from Kaggle ("Most Streamed Spotify Songs 2023") and contains technical audio features, artist metadata, and streaming statistics for nearly 1,000 of the year's most popular tracks.
This project was executed end-to-end within a Python environment:
- Data Importation: The raw CSV data was loaded and structured into a Pandas DataFrame.
- Volume Analysis: Using
.value_counts(), artists were grouped to extract the top 10 hit-makers, visualized through a horizontal bar chart using Seaborn. - Pattern Recognition: A scatter plot was generated to map every individual track based on its 'danceability' (x-axis) and 'energy' (y-axis) to identify clustering and correlations.
- Top Artists: Taylor Swift was the undisputed leader with the highest number of tracks on the 2023 top list, followed by The Weeknd and Bad Bunny.
- The Audio Blueprint: The scatter plot revealed a definitive cluster: virtually zero hit songs had low danceability. An overwhelming majority of global hits possess a 'danceability' score above 60%, and most of these also feature an 'energy' score above 50%.
- Business Conclusion for A&R: Data-driven insights for record labels indicate that investing in and heavily marketing high-danceability tracks yields a statistically higher probability of streaming success compared to low-energy/low-tempo tracks.
(The Python code (.ipynb file) and visual plots are attached in this repository).